Academic Journal
Biologically inspired approaches to automated feature extraction and target recognition
| العنوان: | Biologically inspired approaches to automated feature extraction and target recognition |
|---|---|
| المؤلفون: | Gail A. Carpenter, Siegfried Martens, Ennio Mingolla, Ogi J. Ogas, Chaitanya Sai |
| المساهمون: | The Pennsylvania State University CiteSeerX Archives |
| المصدر: | http://techlab.bu.edu/files/resources/articles_cns/carpenter_martens_mingolla_ogas_sai_2004.pdf. |
| سنة النشر: | 2004 |
| المجموعة: | CiteSeerX |
| الوصف: | Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically combine bottom-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from input labels that are simultaneously inconsistent |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.7117; http://techlab.bu.edu/files/resources/articles_cns/carpenter_martens_mingolla_ogas_sai_2004.pdf |
| الاتاحة: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.385.7117 http://techlab.bu.edu/files/resources/articles_cns/carpenter_martens_mingolla_ogas_sai_2004.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| رقم الانضمام: | edsbas.5983C6BD |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |